Il s’agit d’un essai d’indexation Google
Pour voir à quelle fréquence le site est indexé
ceci est un exemple de volume https://www.goperf.com/wp-content/uploads/2020/10/VOL00001.FDB
Test Index 2 Google
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Test Index Google
Performance Test for a basic WordPress Blog Site running in Docker on a new OVH VPS server
Real Applications benchmarks with Docker
It’s time to write again on this blog.
I plan to benchmark real applications with Docker.
In the meantime, just have a look on this one “WordPress Bare Metal vs WordPress Docker Performance Comparison” by Loadimpact.
The results are amazing: just a slight difference
Bare Metal WordPress Load Impact Results
Docker WordPress Load Impact Results
Loadimpact conclusion:
Based on the results above we can clearly see that the Bare Metal WordPress has a slight performance advantage in comparison to the Docker WordPress.
However what we notice with the Docker graph is the consistency in the performance keeping a relatively flat line in comparison to WordPress Bare Metal
AWS EBS IO performance evaluation
Hammerora TPM – System Comparison overview
AWS is a fantastic offering for those seeking to host their applications.
It is easy and fast :
- to set up (lot of templates, Web Interface, Elastic Load Balancing, API)
- to change (changing EC2 instance type is easy and fast)
- to secure (backup in S3, integrated firewall, Virtual Private Cloud)
- to monitor (Cloud watch)
- to manage (Web Interface, API, Auto Scaling, Ready-to-use databases, …)
And you pay as you use.
But what about performance?
Concerning the Compute performance, I wasn’t worried. Afterwards, AWS EC2 is based on Xen virtualization and the compute performance will be mainly defined by the quantity of host CPU shares and the size of the memory. So it is just defined by the type of instance you choose (micro, large, x large, …).
But in June 2012 (before the launch of new High I/O EC2 Instance Type), I was more concerned by the performance of the IO. EBS (Elastic Bloc Storage) is amazing ; it is so easy to create, to change and to backup. But can we use it for any workload even for heavy database work load?
So I decided to do some tests with SQL Server, SQL IO (Disk Subsystem Benchmark Tool) and Hammerora (an Open Source Database Test Tool).
I used also the tool PerformanceTest from PassMark Software to get some standard benchmark results.
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Scaling heavy database usage with mysql Master Slave configuration
Results Overview
Here I’m experimenting a way to scale a web site that generates an heavy workload on the database.
The idea is to separate the reads from the insert/update sql statements in order to dispatch the reads (select) statements on serveral servers.
I’m going to use a mysql Master / Slave configuration (Read replica).
In order to fully understand the way it will work, to eliminate a maximum of PHP execution impact and to facilitate the test, I decided to use micro PHP benchs made up of simple select and insert statements.
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HAProxy – Experimental evaluation of the performance
HAProxy max throughput overview
When I was evaluating the influence of the number of fronts on the performance of WordPress (see the post Scaling-out WordPress – Performance Measures – Number of fronts influence), I was wondering what should be the number of request per seconds HAProxy was able to sustain.
Some benchmarks on the HAProxy web site show that HAProxy is able to manage up to 25K req/sec at 8KB with a 10GB Nic.
I decided to try to determine the limit on my testing virtualized environment.
In this test, I use the standard “It works” apache page (177 bytes)
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Scaling-out WordPress – Performance Measures – Using Graphite
Results overview
The dashboard above shows what you can do with graphite
At one glance, you have all the indicators you need in real time (I have an auto refresh every 10sec)
In this post, I will show you how to get it by installing and configuring collectd, graphite.
Then we will write a custom performance collector in python to check the response time.
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Scaling-out WordPress – Performance Measures – Number of fronts influence
Results overview
As we know about the number of requests we can expect from a single WordPress front server, it’s time to try to scale out.
Load balancer configuration
I will use haproxy, and as the post HAProxy – Experimental evaluation of the performance shows it, I can setup a small VM with 1vcpu and 512MB.
As WordPress is a stateless product, I don’t need to manage session persistence so a very basic configuration can be used (see Software Installation)
Rem:
In a following post, I will show how to share sessions on an NFS server. For the need of sessions, I will install a e-commerce plugin: WooCommerce
I also deactivated the cache but kept APC.
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Scaling-out WordPress – Performance Measures – Cache influence
Results overview
There is a classical method to increase the performance of web site that consists in caching the HTML pages that are rendering to the client.
That avoids the server to execute the code to request the database and to render the page. The pre-formatted HTML page is generally stored in the file system.
I would like to try it with this workload (that is perfect for caching because there is no specific information linked to the visitor).
I could use many methods:
- caching with apache
- caching with varnish
- caching with a specialized PHP code
I decided to use the third method as there are a lot of existing WordPress plugins that do the job.
I installed the wp super cache plugin and did a new series of tests.
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